tiktoken
zenml
tiktoken | zenml | |
---|---|---|
32 | 33 | |
9,980 | 3,682 | |
6.4% | 2.4% | |
6.7 | 9.8 | |
about 1 month ago | 6 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
tiktoken
- FLaNK AI - 01 April 2024
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GPT-3.5 crashes when it thinks about useRalativeImagePath too much
Their tokenizer is open source: https://github.com/openai/tiktoken
Data files that contain vocabulary are listed here: https://github.com/openai/tiktoken/blob/9e79899bc248d5313c7d...
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How fast is JS tiktoken?
OpenAI's refference tokeniser - https://github.com/openai/tiktoken
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Anthropic announces Claude 2.1 – 200k context, less refusals
ChatGPT presumably adds them as special tokens to the cl100k_base tokenizer, as they demo in the tiktoken documentation: https://github.com/openai/tiktoken#extending-tiktoken
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What is the best way to get an approximate number of tokens for a piece of text?
I want to measure the approximate number of tokens in a piece of text to understand if I will need to modify it before passing it into the context of an OpenAI API call. Tiktoken can do this, but I'm not sure if it's overkill to use that library just for this simple task. I don't need to actually tokenize the text, I just need an approximate count (e.g. within like 1% of the text's actual token length for text that represents the visible text on a webpage).
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Show HN: LLaMA tokenizer that runs in browser
https://platform.openai.com/tokenizer or the official python library tiktoken https://github.com/openai/tiktoken or this JS port of tiktoken https://github.com/dqbd/tiktoken
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Made a GPT-3.5-Turbo and GPT-4 Tokenizer
It's built on top of the tiktoken library and is basically just a lambda function in the backend.
- AiPrice - an API for calculating OpenAI tokens and pricing
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Anyone able to explain what happened here?
"All" is a single token in OpenAI's tiktoken Tokenizer, unrelated to the token for capital "A". Even lowercase "all" is a distinct token from "All" or "ALL."
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Which lib is the tokenizer page using to calculate the tokens?
check tiktoken
zenml
- FLaNK AI - 01 April 2024
- What are some open-source ML pipeline managers that are easy to use?
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[P] I reviewed 50+ open-source MLOps tools. Here’s the result
Currently, you can see the integrations we support here and it includes a lot of tools in your list. I also feel I agree with your categorization (it is exactly the categorization we use in our docs pretty much). Perhaps one thing missing might be feature stores but that is a minor thing in the bigger picture.
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[P] ZenML: Build vendor-agnostic, production-ready MLOps pipelines
GitHub: https://github.com/zenml-io/zenml
- Show HN: ZenML – Portable, production-ready MLOps pipelines
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[D] Feedback on a worked Continuous Deployment Example (CI/CD/CT)
Hey everyone! At ZenML, we released today an integration that allows users to train and deploy models from pipelines in a simple way. I wanted to ask the community here whether the example we showcased makes sense in a real-world setting:
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How we made our integration tests delightful by optimizing our GitHub Actions workflow
As of early March 2022 this is the new CI pipeline that we use here at ZenML and the feedback from my colleagues -- fellow engineers -- has been very positive overall. I am sure there will be tweaks, changes and refactorings in the future, but for now, this feels Zen.
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Ask HN: Who is hiring? (March 2022)
ZenML is hiring for a Design Engineer.
ZenML is an extensible, open-source MLOps framework to create production-ready machine learning pipelines. Built for data scientists, it has a simple, flexible syntax, is cloud- and tool-agnostic, and has interfaces/abstractions that are catered towards ML workflows.
We’re looking for a Design Engineer with a multi-disciplinary skill-set who can take over the look and feel of the ZenML experience. ZenML is a tool designed for developers and we want to delight them from the moment they land on our web page, to after they start using it on their machines. We would like a consistent design experience across our many touchpoints (including the [landing page](https://zenml.io), the [docs](https://docs.zenml.io), the [blog](https://blog.zenml.io), the [podcast](https://podcast.zenml.io), our social media, the product itself which is a [python package](https://github.com/zenml-io/zenml) etc).
A lot of this job is about communicating complex ideas in a beautiful way. You could be a developer or a non-coding designer, full time or part-time, employee or freelance. We are not so picky about the exact nature of this role. If you feel like you are a visually creative designer, and are willing to get stuck in the details of technical topics like MLOps, we can’t wait to work with you!
Apply here: https://zenml.notion.site/Design-Engineer-m-f-1d1a219f18a341...
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How to improve your experimentation workflows with MLflow Tracking and ZenML
The best place to see MLflow Tracking and ZenML being used together in a simple use case is our example that showcases the integration. It builds on the quickstart example, but shows how you can add in MLflow to handle the tracking. In order to enable MLflow to track artifacts inside a particular step, all you need is to decorate the step with @enable_mlflow and then to specify what you want logged within the step. Here you can see how this is employed in a model training step that uses the autolog feature I mentioned above:
- ZenML helps data scientists work across the full stack
What are some alternatives?
tokenizer - Pure Go implementation of OpenAI's tiktoken tokenizer
MLflow - Open source platform for the machine learning lifecycle
daath-ai-parser - Daath AI Parser is an open-source application that uses OpenAI to parse visible text of HTML elements.
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
skypilot - SkyPilot: Run LLMs, AI, and Batch jobs on any cloud. Get maximum savings, highest GPU availability, and managed execution—all with a simple interface.
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
bricks - Open-source natural language enrichments at your fingertips.
Poetry - Python packaging and dependency management made easy
terminal-copilot - A smart terminal assistant that helps you find the right command.
pulsechain-testnet